System and method for carbon emissions exposure determination

ABSTRACT

A system and method may determine the carbon emissions risk to an institution through its lending and investment activities to a plurality of counterparties by, for example, determining carbon emissions data for a number of counterparties and, for each counterparty, determining the carbon emissions risk to the institution. A system and method may determine the proportion of total capital of a counterparty that is being financed by a bank, and multiply this by a carbon emissions measure for the counterparty. Embodiments may be applied to determine optimal investment strategies for managing an institution&#39;s exposures to carbon risk over time. Such measures may be altered or projected using scenarios describing future emissions data.

RELATED APPLICATION DATA

The present invention is a continuation-in-part of prior U.S.application Ser. No. 17/061,145, filed Oct. 1, 2020, entitled “SYSTEMAND METHOD FOR CARBON EMISSIONS EXPOSURE DETERMINATION”, which is beingincorporated herein in its entirety.

FIELD OF THE INVENTION

The present invention relates generally to providing a determination ofan exposure to the “carbon footprint” (e.g., amount of carbon dioxide orother greenhouse gas e-missions) of counterparties.

BACKGROUND OF THE INVENTION

Computer systems and technologies exist to determine carbon emissions ofindividual companies and the “carbon impact” or “carbon footprint”,e.g., the total greenhouse gas (GHG, e.g., carbon dioxide and methane)emissions caused by the companies, including for example emissionsinvolving manufacture, transport, use, disposal, etc. of products andservices. Carbon impact may be expressed for example as carbon dioxideequivalent, but may capture greenhouse effects from gasses other thancarbon dioxide.

For example different companies may have carbon emissions or footprintdata relevant to the organization's operations or sales. Such data maybe collected, calculated and provided by a service, such as the Standard& Poor (S&P) Trucost data service described at trucost.com, providingdata which may be licensed.

Climate change resulting from carbon emissions poses risks to financialinstitutions laving as counterparties (e.g., lendees or obligors)companies which themselves may be subject to risk due to climate change.Financial firms may need to evaluate internally and discloseclimate-related risks. Climate risks may be separated into twocategories:

-   -   Transition risks: The risks to businesses or assets that arise        from policy and legal actions, technology changes, market        responses, and reputational considerations as the international        community seeks to slow the pace of climate change by        transitioning to a lower-carbon economy.    -   Physical risks: The risks to businesses or assets emanating from        changes in climate that are already occurring and are projected        to continue. These can be event-driven, such as increasingly        intense and frequent storms, or related to chronic, longer-term        shifts in precipitation and temperature.

Shared Socioeconomic Pathways (SSP) are scenarios which describe aspectsabout global society, demographics and economics which might change overthe next century due to climate change. These scenarios can provideradiographic and carbon emissions outputs over time under variousclimate risk mitigation occurrences. In one example, the SSP scenariosoutline goals of limiting warming to below 2° C., with the SSP1-1.9scenario (1.3-1.4° C.) being more aggressive in its climate riskmitigation assumptions than the SSP1-2.6 scenario (1.7-1.8° C.).Financial firms may need to mitigate their exposures and risks toclimate change based on not only current situations, but projectedscenarios. Different scenarios for the same time period or date mayprovide different data.

Financial institutions now face the tasks of identifying, managing anddisclosing, and mitigating their exposures to climate change. Given thenescient state of understanding climate data as it relates to financialportfolios, systematic and quantitative approaches and tools tointerpret and attribute climate data are needed to incorporate climatechange metrics into business planning and portfolio managementpractices. Prior systems for analyzing carbon emissions data andfinancial exposure data do not form a coherent view on institutions'historical performance and future plans to manage climate change-relatedrisks nor provide a concrete portfolio strategy through quantitativeoptimization that can achieve financial institutions' targets whilestill allowing for growth and mitigating climate change-related risks.

SUMMARY OF THE INVENTION

A system and method may determine the carbon emissions risk to aninstitution of a plurality of counterparties to the institution (e.g.,where the institution provides loans or financing to the counterparties)by for example determining carbon emissions data for a number ofcounterparties, and for each counterparty, determining the carbonemissions risk to the institution by multiplying the carbon emissionsdata for the counterparty (e.g., tonnes of carbon dioxide emitted peryear per unit of revenue) by the exposure of the institution to thecounterparty.

A system and method may improve prior carbon footprint calculationtechnology and financial analysis technology, and may provide atechnology solution which may for example combine carbon emissions datawith data regarding counterparties to enable an institution to analyzeand alter its carbon risk and the amount of carbon emissions itfinances. Systems and methods may optimize the amount of carbonemissions an institution finances, using models such as a quantitativemodel and future scenarios, given business and practicalityconsiderations, enabling the institution to analyze, strategize andalter its carbon risk profile.

A system and method may enable incorporation of externally sourcedcarbon scenarios into a scenario analysis.

Targets or limits may be established for financial investment ininstitutions based on an optimization method so as to reduce thefinanced emission while meeting criteria for the portfolio risk andreturn profile on a forward looking basis. In addition to lending bookexposures, embodiments may determine a firm's carbon emission risk byanalyzing a financial institution's trading book exposures andcounterparty current exposures of the trading book.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of embodiments of the disclosure are describedbelow with reference to figures listed below. The subject matterregarded as the invention is particularly pointed out and distinctlyclaimed in the concluding portion of the specification. The invention,however, both as to organization and method of operation, together withobjects, features and advantages thereof, may best be understood byreference to the following detailed description when read with theaccompanied drawings.

FIG. 1 depicts a carbon footprint exposure calculation system accordingto embodiments of the present invention.

FIG. 2 depicts a display, e.g., presented to a user, showing carbonemissions risk such as a Carbon Intensity Index calculated over time foran institution, according to embodiments of the present invention.

FIG. 3 depicts a display, e.g., presented to a user, showing a carbonintensity by industry calculated over time for an institution, accordingto embodiments of the present invention.

FIG. 4 depicts a display, e.g., presented to a user, showing a CarbonIntensity Index calculated over time comparing institutions, accordingto embodiments of the present invention.

FIG. 5 depicts a display, e.g., presented to a user, showing a modelover time of a portfolio as counterparty weights are changed, accordingto embodiments of the present invention.

FIG. 6 depicts a display, e.g., presented to a user, showing carbonintensity over time for an owning institution, and other data, accordingto embodiments of the present invention.

FIG. 7 depicts a display, e.g., presented to a user, allowing modellingof carbon data at a point-in-time, according to embodiments of thepresent invention.

FIG. 8 depicts a display, e.g., presented to a user, allowing modellingof carbon data over future time periods, according to embodiments of thepresent invention.

FIG. 9 depicts a display, e.g., presented to a user, allowing aDistribution Analysis calculation and display, where a user analyzes thefinanced emission of a specific counterparty of an institution,according to an embodiment of the present invention.

FIG. 10 shows a flowchart of a method according to embodiments of thepresent invention.

FIG. 11 shows a high-level block diagram of an exemplary computingdevice according to some embodiments of the present invention.

FIG. 12A depicts a display, e.g., presented to a user, showing twoscenarios of projected carbon emissions for the Energy sector over time,according to embodiments of the present invention.

FIG. 12B depicts a display, e.g., presented to a user, showing theaggregate sum of projected financed emissions for all counterpartiesover time, according to embodiments of the present invention.

FIG. 13 depicts a display, e.g. presented to a user, showing possiblepathways for projected Financed Emissions which meet a target scenario,according to embodiments of the present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn accuratelyor to scale. For example, the dimensions of some of the elements may beexaggerated relative to other elements for clarity, or several physicalcomponents may be included in one functional block or element. Referencenumerals may be repeated among the figures to indicate corresponding oranalogous elements.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components,modules, units and/or circuits have not been described in detail so asnot to obscure the invention. For the sake of clarity, discussion ofsame or similar features or elements may not be repeated.

Although embodiments of the invention are not limited in this regard,discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium thatmay store instructions to perform operations and/or processes. Althoughembodiments of the invention are not limited in this regard, the terms“plurality” and “a plurality” as used herein may include, for example,“multiple” or “two or more”. The term set when used herein may includeone or more items. Unless explicitly stated, the method embodimentsdescribed herein are not constrained to a particular order or sequence.Additionally, some of the described method embodiments or elementsthereof can occur or be performed simultaneously, at the same point intime, or concurrently.

FIG. 1 depicts a carbon footprint tracking and calculation systemaccording to embodiments of the present invention. Some of thecomponents of FIG. 1 may be separate computing devices such as serversand others may be combined into one computing device. Some modules inFIG. 1 may be included in other computing devices than as shown.

Server 10 may include one or more processors 12; and a credit riskmanagement database 14 storing, e.g., financial information,counterparty information, debt and equity information, exposureinformation, and possibly other information; a mapping database 16 whichmay map, e.g., Trucost company identifiers to internal (to the owninginstitution) company identifiers; and possibly other mappinginformation. Server 10 may accept data from external sources such as anSEC (Securities and Exchange Commission) source 5 and a carboninformation database 7 (e.g., the Trucost database), or other sources.User terminals 30 may be connected to server 10, for example by network9, and may display reports to users, and allow users to inputinformation or queries, or request information or otherwise control aprocess of determining carbon risk exposure, for example via a GUI.Components of FIG. 1 such as databases, server 10, terminals, and aprocessor may be or include components such as depicted in FIG. 11.Server 10 may determine carbon risk exposure, and generate reports(e.g., provided as screen displays on terminals 30). In otherembodiments, other configurations of components and databases may beused; for example in place of a server processing may be performed by adesktop or laptop computer.

Embodiments may provide quantitative measures that leverage both carbonemission data from external sources and the internal financial exposuredata of a given institution such as a bank to capture the bank'sexposure to climate risk through its financial transactions withcounterparties. For example, by lending to a utilities company that is aheavy GHG emitter, a bank can be exposed to heightened credit riskshould there be a carbon emission tax imposed on such companies.Embodiments may measure such risk over time and provide useful output.Two example quantitative indices of carbon emissions risk produced byembodiments may include:

-   -   Carbon Intensity Index, combining sector-level exposures of a        bank or institution with the sector-average carbon intensities        sourced from an external source or vendor, and showing how a        bank's portfolio mix over time affects its climate risk profile.        This may be used to compare different banks' trends in climate        risk profiles. A carbon intensity index may measure relative or        absolute counterparty exposures, and may be expressed as metrics        such as a relative measure. A relative measure may use sector        exposures in %. This variation may express the relative        vulnerability of a bank, regardless of its size, where % may be        defined as a sector's exposure divided by the total exposure        across all sectors for a specific institution or in general,        across all data outside of an institution: e.g., 20% of a bank's        total exposure could be coming from the Energy sector. A carbon        intensity index may be expressed as an absolute measure,        utilizing sector exposures in currency such as dollars, which        may take into account the absolute size of a bank.    -   A Financed Emission Index combining a bank's or institution's        internal granular exposure data with company-level carbon        emission information sourced externally, providing a detailed        view on the amount in carbon emissions the bank is funding        through its lending activities. This metric can be leveraged to        provide guidance on how to manage towards carbon targets or        neutrality for the bank, as well as to inform future compliance        and reporting requirements. This may measure the amount of        carbon emission sponsored indirectly through a firm's financing        and/or trading activities.

Both the Carbon Intensity Index and the Financed Emission Index measurehow carbon intensive a bank's investments are, but from differentperspectives. A Carbon Intensity Index may capture a bank's investmentsfrom a financial risk perspective, particularly in terms of financialexposure to carbon intensive industries. For example, banks that haveexposures that are skewed towards industries such as Utilities, Energy,or Materials may be exposed to heightened financial risk if a carbon taxis imposed, since companies in those industries are usually heavy carbonemitters. Also, to capture the financial risk aspect, carbon intensities(e.g., emission/revenue) may be used in the calculation, not carbonemissions. A Financed Emission Index may measure the amount of carbonemission a bank is responsible for by investing in individual companiesor by engaging in trading activities with counterparties, by accountingfor the size of the company the bank is lending to or the size of thetrading exposure (e.g., by taking the ratio ofinvestment/(equity+debt)), but may not take into account the revenue ofthe company (e.g., by using carbon emission instead of carbonintensity). With proper carbon emission data, the Financed. EmissionIndex can be leveraged to provide guidelines on how a bank can worktowards carbon neutrality.

While lending is described herein as one exposure, other risks orexposures may be measured, such as investments, equity holdings, andtrading book exposures.

Various data sources may be used, such as:

-   -   The internal financial information of the institution operating        an embodiment of the present invention (which may be termed the        “owning institution”), e.g., the institution's credit portfolio.        This information may be from the owning institution's credit        portfolio data and internal databases;    -   The institution's trading book portfolio. This information may        be from the owning institution's trading book data and internal        databases;    -   Sector-level credit lending exposures of the owning institution        and other “peer” or competitor institutions or banks;    -   Financial statement data of the owning institution's        counterparties;    -   Carbon emission data for counterparties, such as from Trucost (a        subsidiary of Global S&P);    -   Information on counterparties, including among other variables,        data containing point-in-time information of the counterparty's        sector, rating and exposure size;    -   Sector-level credit lending exposures of banks which may be        collected from their 10-Q/K SEC filings. Such information may be        in the commercial credit exposure section. Banks report their        committed lending exposure to each sector regularly; and    -   Financial statement data of the owning institution's        counterparties which may be stored internally to the        institution, including data such as total capital of the company        as defined by for example equity plus debt or adjusted total        debt.

The Trucost service or another service or database may provide data onthe amount of carbon emissions a company produces over a certain period.Other services or databases may provide projected data describing amountof carbon emissions a company may produce in a future period. Forexample, the International Institute for Applied Systems Analysis or theInternational Energy Administration may provide data, for example viadatabases, that projects over a future period of time the amount ofcarbon emissions (e.g. for industry, groups of companies defined by GICcodes, category, etc.) expected under various assumptions including;energy usage, economic growth, population size, etc., resulting in ascenario. Such data may include scenarios, which may include carbonoutput over time for groups, industries, etc. Data from various externaldatabases or services may be applied together to derive a projection ofemissions for a counterparty. For example, carbon emissions for acounterparty (e.g. sourced from Trucost or another service) may beapplied to the rate of change in projected emissions. Analyzinghistorical carbon emissions rate of change from services such as Trucostmay derive a projection of emissions for the counterparty. Such aservice may also provide other climate metrics for analytics, includingenergy usage, physical risk sensitivity, etc. Typically, the carbonemissions values received from such a service for or assigned to acompany are on a yearly basis (e.g., an annual carbon emission value)but other periods or measures measuring the carbon emissions of amerchant may be used. A set of example values received from such adatabase for each company may include Global Industry ClassificationStandard (GICS) category and subcategory, annual tonnes (metric tonnes)of carbon or carbon equivalent emitted, such as the sum of scopes 1, 2and 3, annual revenue in millions of dollars, and carbon or carbonequivalent emitted per dollar of revenue (e.g., per year), all typicallyon an annual basis (other time periods may be used). The set of examplevalues may be aggregated to groups of counterparties within an industryor sector using GICS codes, or to other levels of aggregation such ascounterparty region or rating. The internal lending exposure of eachcounterparty may be calculated by aggregating across the products—e.g.,different loans or other financial products—under this counterparty'sname, at the company level. While dollars are used as an examplecurrency herein, other currencies may be used.

External data (e.g., from Trucost) may need to be mapped to internaldata; for example counterparty ID and sector IDs, or sovereign IDs ordefinitions may be different across databases internal to an investorand external. Similar mappings may be performed for a Firm's tradingbook exposures to external data sources. Mappings may be done in advanceof carbon calculations, creating an internal table mapping external IDsto internal IDs. A category of entities may be a sector or industry;sectors may be defined by the Global Industry Classification Standard(GICS); banks may report sectors in different fashions, but may bemapped by a calculating institution to the standard GICS.

Total annual carbon emissions for a company may be categorized as Scope1, Scope 2 and Scope 3 categories of information within the GreenhouseGas Protocol. Scope 1, or Direct CMG emissions, may include all directGHG emissions by a company, or under direct control of a company, suchas fuel combustion directly caused by the company (e.g., at a factorymanufacturing a t-shirt), company vehicles and fugitive emissions. Scope2, electricity indirect GHG emissions, may cover indirect GHG emissionsfrom consumption of purchased electricity, heat or steam. Scope 3, allother indirect emissions from activities of the organization, may occurfrom sources that the organization does not own or control, such asbusiness travel, procurement, waste and water, or from purchasedcommodities (e.g., source materials such as beef or cotton). For eachcompany or counterparty scope 1, 2 and 3 values (in units of for exampleweight or mass of CO2 or equivalent per time period, e.g., metric tonnesCO2 per year) may be received from a public or subscription service(e.g., the S&P service). Example data from Trucost's carbon emissiondataset may include company information (name, ID (identification),sector, country), financial information (revenue, currency) and emissioninformation (carbon emitted at different scopes, carbon intensity).

In one embodiment, the carbon intensity of a company or counterparty maymeasure the amount of greenhouse gas it emits to generate one unit ofrevenue, typically over a period of time. A company's carbon intensitymay be its carbon emissions (e.g., scopes 1, 2 and 3 added together, butother measures may be used) divided by its total revenue, per year;other time frames may be used in which both carbon emission and carbonintensity and revenues for a given company are taken from the same timeframe. For a financial institution that lends to other companies, e.g.,counterparties, its exposures to all its counterparties can besummarized in terms of those counterparties' carbon intensities.Similarly, for a financial institution that has exposures to othercompanies through its trading activities, its exposures to all itscounterparties can also be summarized using the same logic.

An embodiment may calculate a carbon intensity index for an institutionas the ratio of the amount of loans by the institution to a counterpartyto the total amount of loans made by the institution; this may bemultiplied by the carbon intensity of the counterparty. In someembodiments this may be summed or aggregated across all or manycounterparties for an institution to create another measure of a carbonintensity index. In one embodiment, for each counterparty, a process maydetermine the carbon emissions risk to the institution by multiplyingthe carbon emissions data for the counterparty by the exposure of theinstitution to the counterparty (e.g., amount lent to the counterparty)and dividing the exposure by the total amount of exposure made by theinstitution. Exposure may include loans, swaps, or other trading bookexposures. The carbon intensity index of a lender or owning institutionmay be calculated as a weighted average of its counterparties' carbonintensities, with the weights being the proportions of the lender'sexposure to its counterparties, as in example Formula 1:Carbon Intensity Index=Σ_(i) Exposure_(i)/Total Exposure×CarbonIntensity_(i)   Formula 1

In Formula 1 Exposures may be a bank's financial exposure tocounterparty i, which may be cumulative exposure (e.g., across multipleloans or other exposures to the counterparty). A firm's financialexposure to trading for a counterparty i may be defined as deltaexposure as known in the art or current credit default exposure that thefirm has to counterparties on derivatives contracts; lending exposuremay be the total loan commitment amount made by the Firm to acounterparty. Exposure, when used in a formula as a dollar amount, mayindicate the amount, in currency, that the institution has lent to orotherwise invested in (e.g., via a swap) in the counterparty. TotalExposure may be the sum of all counterparties' exposures for the bank.Apart from the lending exposure, total trading exposure may be the sumof delta exposures which may be for example the value change or profitand loss (“P&L”) per unit move in the underlying price/volatility.Carbon Intensity_(i) may be the carbon intensity of counterparty i, forexample obtained from vendor carbon emission data, e.g., Trucost; forexample in units of tons or tonnes of carbon emissions/revenue incurrency/year. As with other measures discussed herein, time periodsother than years may be used. Carbon Intensity Index may be measured inunits of tons or tonnes of carbon/unit of revenue (e.g., millions ofdollars)/time period (e.g., year). While in Formula 1, and in the otherFormulas discussed herein, the investment or exposure correlated to acarbon intensity is financial exposure to a counterparty, otherinvestments or exposures may be used, as discussed elsewhere. Therelevant investment or exposure (e.g., Exposure_(i)) is tied in therelevant formula to a relevant carbon intensity (e.g., CarbonIntensity_(i)) that represents some measure of the carbon emissions ofthe investment of exposure. For example, if the investment or exposureis a swap or derivatives contract, the Carbon Intensity_(i) may be thecarbon intensity of the counterparty or the substance of the swap (e.g.,e.g., a commodity); if the investment or exposure is a delta-basedexposure, the Carbon Intensity_(i) may be the carbon intensity of thesector or subsector for the company (e.g., an average of companies inthe sector or subsector); and if the investment or exposure is a tradingbook exposure, the Carbon Intensity_(i) may be the carbon intensity ofthe sector or subsector for the company.

Carbon Intensity according to Formula 1 may be calculated using current(e.g. in the present day) carbon emissions data for a counterparty orinvestee, or carbon emissions data that is projected for a future dateor time period, e.g. according to a scenario. This modification based onprojected data may also occur for Financed Emissions, or other measures,displays, models or outputs discussed herein. For example, a scenariocorresponding to a future date or time period (e.g. the year 2035) mayinclude data allowing for future (e.g. at a time corresponding to thescenario) current carbon emissions data for a counterparty or investeeto be projected or calculated. Alternately, a scenario may includefuture carbon emissions data for an investee or counterparty. A scenariomay provide data relevant to a future date which may be used to modifyor project current data to conform to that future date, for example,using the sector projected emissions as discussed with respect toFormula 4.1 herein which may allow adjusting emissions of a counterpartyor investee according to some estimate of future emissions (e.g. Formula4.1a). One or more different scenarios may be used or selected, eachdescribing a different projection of carbon emissions over time for asector or other grouping, or for a company; each of these scenarios mayinclude different carbon outputs at different times. Each of these timesmay be used to project a measure such as carbon intensity at therelevant time in the future, adjusting the Formula 1 calculation for thevarious particular points in time in the future.

In some embodiments of the invention, Carbon Intensity_(i) in Formula 1or elsewhere may be the projected (e.g. projected to or based on afuture date or time period) carbon intensity of counterparty i. Carbonemissions of the investment of exposure may be projected from vendorcarbon emissions data (e.g. International Institute for Applied SystemsAnalysis) which may in turn be projected sector carbon emissions basedon scenarios (e.g. SSP1-2.6). For example, the use of future sectorprojected carbon emissions may be used to adjust or modify Trucost'spresent data to produce projected data. For example, if the exposure isa delta-based exposure, Carbon Intensity_(i) may be provided orcalculated for the sector or subsector for the company projectingdecades into the future (e.g. 30 years) based on data in the SSP1-2.6scenario. A general projection trend may for example, under thisscenario, project lower Energy sector carbon emissions. For example,energy sector companies are increasingly adopting carbon neutralinitiatives and operation methodologies to reduce their carbonfootprint, therefore, the Carbon Intensity_(i) for a counterparty in theEnergy sector may reflect this trend and be projected as part of theinstitution's Carbon Intensity Index. Formula 1 may therefore provideprojected Carbon Intensity Index for a future period of time.

Formula 1 above, and the other formulas discussed herein, may becalculated for Scope 1, Scope 2, and Scope 3, and in addition thecombination of Scopes 1, 2 and 3; if other carbon emissions data isdivided into categories different calculations may be performed forthese categories. While i in the various formulas discussed hereintypically represents each counterparty in the portfolio of aninstitution (or each counterparty for which the institution has accuratedata), i in other embodiments may represent other entities such as anentire category, industry, sector, etc. such that each i causes theformula to add a measure of the entity's carbon emissions. Thedesignation for i may also be represented as an individual loan,position or a trade, such as the delta exposure for a trading position.For example, carbon emissions may be aggregated across companies in asector to produce sector i's carbon emissions. For some lendingcounterparties, where financial information is not available, carbonintensity, financed emissions, or other formulas may not be able to becalculated.

A variation of a carbon intensity index may use absolute dollar valueexposure for each counterparty as the weight, instead of using relativeexposure in percentage. An example version of this carbon intensityindex is expressed in Formula 2, having the same variable definitions asFormula 1:Carbon Intensity Index=Σ_(i) Exposure_(i)×Carbon Intensity_(i)   Formula2

This variation of carbon intensity index takes into account the size ofthe institution or bank. This variation may also use as input projectedor estimated future carbon intensity data, for example as calculatedusing methods discussed herein with respect to example Formula 4.1 (e.g.Formula 4.1a which may allow adjusting emissions of a counterparty orinvestee according to some estimate of future emissions). As with otherFormulas discussed herein, Formula 2 can be based on a differentinvestment or exposure. As a result, larger banks tend to have highercarbon intensity indices, are more carbon intensive and are moresensitive to carbon regulations. Small banks may have lower absoluteexposure to carbon and carbon regulations.

Formula 2 may, as opposed to normalizing by percentage, account fortotal dollar amount. The units for Formula 2 are not absolute tons ofcarbon, but rather exposure (in currency)*carbon emissions/revenue (incurrency)*time period (e.g., e.g., year). (In this example formula itmay be confusing to cancel the two currency values, as they measuredifferent things.)

In order to determine variables such as Exposure_(i) and Total Exposurefor specific counterparties, an owning institution may access its owninternal databases, for example which may contain exposure and lendingspecific data at the transaction level. An owning institution may makeuse of its internal lending exposure data which are more granular thanthe SEC 10-Q/K reported exposures by sector. However, for purposes of anembodiment performing competitive analysis of an owning institutionagainst other institutions, and to ensure direct and consistentcomparisons between banks' indices can be made, preferably sector or SEC10-Q/K exposure data, available from public sources or databases. Forexample, individual institutions' investor relations websites may beused to compare the owning institution to other banks. An example ofsuch a database is the United States Securities and Exchange CommissionFiling Form 10K Annual Filing for fiscal year 2015 commission, filenumber 1-9924 for Citigroup obtained from the Citigroup investorrelations web page.

In one embodiment, counterparties may be divided into sectors, forexample Communications Services, Consumer Discretionary, ConsumerStables, Energy, etc. Another embodiment may divide trading exposuresinto asset classes including but not limited to Commodity, Equity, FX(currency trading, an example being on the Forex exchange), InterestRates, or Credit Trading Books. An embodiment may measure carbonexposure of the aggregation of counterparty current exposures in thetrading book to categories, industry sectors (e.g., communicationsservices, consumer discretionary, energy, etc.). An embodiment maymeasure carbon exposure of an aggregation of exposures in any or all ofthese groups (trading book delta exposures, counterparty currentexposures, and counterparty lending exposures) to the sovereign level,or alternatively to the firm level. Sectors may be based on generalsectors and/or based on standards such as the Global IndustryClassification Standard (GICS) which is an industry taxonomy developedin 1999 by MSCI Inc. and S&P. The GICS structure includes of 4 levels:sector, industry group, industry, and sub-industries.

The various measures (e.g., Carbon Intensity Index, Financed EmissionIndex, etc.) may be measured per counterparty, per sector, or in otherways. In some embodiments a carbon emissions amount such as carbonequivalent emitted per dollar of revenue per year may be calculated foreach sector, and this may be used in other calculations describedherein. To obtain a sector-level carbon emissions value or intensity, anexternal database (e.g., Trucost) may be consulted, or each sector'scarbon emissions may be calculated by for example averaging companies'values or intensity within the sector, or performing a weighted sum forall companies or counterparties in the sector (e.g., as defined by GICSstructure)

Carbon emission data for companies may be averaged within a sector toarrive at a sector-average carbon emission measure. In addition, usingfor example internal data, bank exposure and financial data may beaggregated to sector level. Both emissions and exposure outputs may bemapped on a sector level to create sector-level carbon exposuremeasures. For example, for a given bank or owning institution, and foreach sector, an index component, e.g., measure, may be calculated (e.g.,Carbon Intensity Index or Financed Emission Index). For an owninginstitution, each counterparty in a sector may be mapped to its carbondata, a calculation performed, and the data aggregated to sector levelmeasures. The aggregation may weight each sector by the institution'sexposure to each sector. For a competitor institution, where possiblyonly sector level information is available to an owning institution, thesectors' calculated carbon data may be used along with the sectorexposures to calculate a measure. Index components may be aggregated oradded across sectors to arrive at a final measure (e.g., CarbonIntensity Index) for each institution. For the owning institution, sincemore granular exposure data may be available internally to thatinstitution (e.g., specific exposures to specific counterparties), theindex can be constructed with company-level information, instead ofsector-level information (for competitor institutions company-basedexposure may not be available). Such measures may be calculated for eachof Scope 1, Scope 2 and Scope 3. Sector level displays and measures mayalso use as input projected or estimated future carbon intensity oroutput data, for example as calculated using methods discussed hereinwith respect to example formula 4.1.

The various carbon measures discussed herein such as a carbon intensityindex or financed emissions may be calculated based on investments orexposures other than lending exposure to a counterparty. For example,measures of carbon exposure may be based on trading book exposures suchas counterparty current exposures (“CE”), commodity (“CM”) investments,equity (“EQ”) or stock investments, interest rate investments, or credit(CR) trading books; or deltas (or other “greeks”, such as gamma) orderivatives of the commodities, equities, foreign exchange (“FX”) orcurrency investments, interest rates (“IR”), or credit trading books; orcounterparty current exposures of a firm's trading book. Such data orexposures may be stored in the firm's internal databases. For each suchexposure, the source carbon intensity data (e.g., sector average carbonintensities) is the same, but the carbon data may be adjusted ormanipulated to suit the relevant investments or exposures. Suchcalculations may be done at different granularities, such as GICSsector, GICS sub-industry, company or even position levels. Such“greek”, stock investment, or other data, may use as input projected orestimated future carbon intensity or emissions data, for example ascalculated using methods discussed herein with respect to exampleFormula 4.1. For example such greek, non-lending or stock investmentdata may be modified or projected to a future time period by havingcurrent carbon data for a company modified by future estimated datarepresenting the industry or other group to which the specific companybelongs.

An example of a non-lending investments or exposures based on GICSsector level granularity includes an assumption that an examplecommodities trading book includes three companies, with two utilitiessector companies and one financials sector company. The owninginstitution may have multiple positions and/or trades with a singlecompany. The delta exposure of one position may be defined as the profitand loss (“P&L”) of that position given a 100% increase of the price orvolatility of the position's underlier. (The 100% increase may be in theunderlier's price or the underlier's volatility, depending on theproduct type (since certain derivatives are priced on volatility). Deltaexposures of the equities and FX trading portfolios may be definedsimilarly as above.

Table 1 depicts example exposures to the three example companies, withCM representing commodities. While the asset class in example Table 1 iscommodity, the company need not be a commodity-based company; rather theasset may be a swap with the company based on the CM asset class, or, inanother example, a swap based on IR (interest rates):

TABLE 1 Asset Company Position GICS MS Delta Class Name ID SectorExposure CM Company ABC 39263319 Utilities 100 CM Company ABC 39263320Utilities 80 CM Company DEF 39663318 Utilities −60 CM Company XYZ39200211 Financials 200 CM Company XYZ 39200212 Financials −50

Table 2 depicts example average carbon intensities for various sectors:

TABLE 2 GICS Sector Average Carbon Intensity Communication Services 80Consumer Discretionary 380 Consumer Staples 990 Energy 1030 Financials50 Health Care 160 Industrials 390 Information Technology 190 Materials1560 Real Estate 180 Utilities 3260

Using the data in Table 1 and Table 2, a version of Formula 1 above canbe calculated as an example application of Formula 1:

${{\frac{{100} + {80} - {60}}{270}*3260} + {\frac{{200} - {50}}{270}*50}} = {147{6.6}7}$In this example application of Formula 1, the first term is the sum ofthree exposures in the Utilities category divided by the sum of allexposures (100+80−60+200−50=270), multiplied by the carbon intensity ofthe category Utilities, and the second term is similar, for theFinancials category. In Formula 1, carbon emissions data correspondingto one or more exposures to the institution may be determined orgathered (e.g., in Table 1) the carbon emissions risk to the institutionmay be calculated or determined by multiplying the carbon emissions datafor the exposures by the exposures themselves. In the specific exampleof Formula 1 above, this is done for each of a number of sectors orindustries: exposures are grouped by category, sector or industry andsummed within each grouping, and for each grouping of exposurescorresponding to an industry or sector, the sum of the exposures in thegrouping may be divided by the sum of all exposures across all groupings(e.g., the sum of the exposures in the formula) to produce a normalizedgrouping sum. The multiplying of the carbon emissions data for theexposures by the exposures may then be performed by multiplying thecarbon emissions data for an industry or sector corresponding to agrouping by the normalized grouping sum associated with that industry orsector. Other formulas discussed herein can similarly be modified toinclude different exposures. Such exposures to commodities, or interestrates (e.g. below) may be projected into the future by being modifiedfor example as calculated using methods discussed herein with respect toexample Formula 4.1.

An example of interest rate investments or exposures includes anassumption that an interest rates trading book includes three companies,two utilities companies and one financials company. The owninginstitution may have multiple positions and/or trades with a singlecompany. The delta exposure of one position may be defined as the P&L ofthat position given a 1 basis point (or 0.01%) increase of the price orvolatility of the position's underlying rate. (The 100% increase couldbe in the underlier's price or the underlier's volatility, depending onthe product type, since certain derivatives are priced on volatility.).The delta exposures of the credit products (CR) trading portfolio may bedefined similarly as above. A hypothetical commodities trading bookportfolio is shown in Table 3, where asset class IR is interest rate:

TABLE 3 Asset Company Position GICS MS Delta Class Name ID SectorExposure IR Company ABC 39263319 Utilities 5 IR Company ABC 39263320Utilities −1 IR Company DEF 39663318 Utilities 2 IR Company XYZ 39200211Financials 3 IR Company XYZ 39200212 Financials 1The example carbon intensity data used is the same as those in Table 2above. The version of Formula 1 above can be calculated as an exampleapplication of Formula 1:

${{\frac{5 - 1 + 2}{10}*3260} + {\frac{3 + 1}{10}*50}} = {1976}$In this example application of Formula 1, the first term is the sum ofthree exposures in the Utilities category divided by the sum of allexposures, multiplied by the carbon intensity of the category Utilities,and the second term is similar, for the Financials category.

An example of a counterparty book current exposures (CE) carbonintensity index includes the example of a counterparty book includingthree companies, with two utilities and one financials. The Owninginstitution may have multiple positions and/or trades with a singlecompany. The current exposure (CE) of a position may be the larger ofzero, or the market value of the transaction with a counterparty thatwould be lost upon the default of the counterparty, assuming no recoveryon the value of the transaction in bankruptcy. The CEs are associatedwith the trading book but are different from the delta exposuresmentioned above. A hypothetical CE trading book portfolio is shown inTable 4:

TABLE 4 Company Position GICS Current Name ID Sector Exposure (CE)Company ABC 39263319 Utilities 5000 Company ABC 39263320 Utilities −1000Company DEF 39663318 Utilities 2000 Company XYZ 39200211 Financials 3000Company XYZ 39200212 Financials 1000

In the following example application of Formula 1, the first term is thesum of three CE exposures in the Utilities category divided by the sumof all exposures, multiplied by the carbon intensity of the categoryUtilities, and the second term is similar, for the Financials category:

${{\frac{{5000} - {1000} + {2000}}{10000}*3260} + {\frac{{3000} + {1000}}{10000}*50}} = {1976}$

In options trading, delta refers to a change in the price of an optioncontract per change in the price of the underlying asset. Gamma refersto the rate of change of delta. A delta may measure the theoreticalchange in premium for each $1 change in the price of the underlyingasset or security. Table 5 below shows example deltas for example assetclasses:

TABLE 5 Asset Class Definition CM P&L per 100% move in underlyingprice/volatility CR P&L per 1 bps move in underlying price/volatility EQP&L per 100% move in underlying price/volatility FX P&L per 100% move inunderlying price/volatility IR P&L per 1 bps move in underlyingprice/volatility

FIG. 2 depicts a display, e.g., presented to a user, showing carbonemissions risk such as a Carbon Intensity Index calculated over time foran institution, according to embodiments of the present invention.Referring to FIG. 2, a Carbon Intensity Index is calculated by a system(e.g., server 10), and displayed for each of Scope 1 (line 200), Scope 2(line 202) and Scope 3 (line 204), and all three scopes added oraggregated (line 206). The X axis is time and the Y axis is the indexvalue: an index value may be the same units for carbon intensity, forexample using tonnes carbon (in a year)/$mm revenue/time period (e.g., ayear). The data depicted in a display such as in FIG. 2 may becalculated according to Formula I above, but also can be calculatedaccording to Formula 2 above (such that the Y axis measures exposure (incurrency)*carbon emissions/revenue (in currency)*time period (e.g.,year) or other measures. As with other graphs discussed herein usingScopes 1, 2 and 3, data may be displayed individually as one of scopes1, 2 and 3 or an addition of Scopes 1, 2 and 3. Such a display, andother displays discussed herein, may be projected into the future byhaving underlying data modified to correspond to future estimates forcarbon emissions for the relevant counterparties, for example ascalculated using methods discussed herein with respect to exampleFormula 4.1.

FIG. 3 depicts a display, e.g., presented to a user, showing carbonintensity by or per category (e.g. industry or sector) calculated overtime for an institution, according to embodiments of the presentinvention. Referring to FIG. 3, a Carbon Intensity Index is dividedamong different industries or sectors 210 (e.g., utilities, real estate,materials, information technology, etc.) across time along the X axis.The Y axis is the index value, such as percentage exposure x tons ofC02/$ million revenue/time period. FIG. 3 is calculated according toFormula 1 described herein, but also can be calculated according toFormula 2 described herein (such that the Y axis measures percentageexposure x carbon emissions/time period (e.g., year), or other measures.Mapping of carbon emissions and exposure data and aggregation ofexposures may be also performed for trading counterparty currentexposures and trading book delta exposures, in addition to for lendingexposures. Attachment of exposures to carbon emissions at the companylevel may be performed for counterparty trading book exposures.

The display of graph of FIG. 3, and other displays shown herein, may beadapted to display carbon intensity measures by sector based on assets,investments or exposures other than those depicted in FIG. 3. Forexample, an embodiment may calculate and display a graph such as thatshown in FIG. 3, but with carbon exposure based on other exposures suchas trading book exposures such as CE, CM, EQ, FX, interest rate, CR; ordeltas or other “greeks”, or other exposures discussed herein. Such adisplay may be modified and projected into the future by havingunderlying data modified to correspond to future estimates for carbonemissions for the relevant counterparties, for example as calculatedusing methods discussed herein with respect to example Formula 4.1.

FIG. 4 depicts a display, e.g., presented to a user, showing a CarbonIntensity Index calculated over time comparing institutions, accordingto embodiments of the present invention. The X axis depicts time and theY axis depicts carbon intensity, according to Formula I above, in unitssuch as tons or tonnes of carbon/unit of revenue (e.g., millions ofdollars)/time period (e.g., year). Other formulas, such as Formula 2above, or other formulas, may be used. Various institutions 240 may havecarbon intensity data presented. In some embodiments, data may begathered by an automatic process from, e.g., an SEC source 5 maintainedby the SEC, providing sector-level credit lending exposures ofinstitutions other than and/or including the owning institution, such as10-Q/K filings, e.g., the commercial credit exposure section of suchfilings. An automatic process, e.g., executed by server 10, may read(and digitize or use optical character recognition, if the source datais not computer readable) and, for each institution, extract lendingexposure by sector. In this manner public information from the SEC orother filings may be used to construct carbon intensity indexes for peerinstitutions among institutions 240.

Displays of data, and individual data, may be broken down by, ordisplayed by, various factors. For example, a counterparty's rating(e.g., a bond rating, such as B-rating, AA rating, etc.) may be used todivide data. Mean or median data, e.g., from Trucost as categorized byGICS or another chosen division factor, may be used as proxy data, anddisplays may include one or the other or both. Calculating proxy datamay include calculating carbon emissions data for the category (e.g.sector or industry) in which a counterparty is a member and assigningthe proxy carbon emissions data to the counterparty.

An embodiment may calculate exposure for an institution as the ratio ofa) the amount of loans by the institution to a counterparty (e.g.,“investment”), to b) the investee equity plus investee debt; this may bemultiplied by the carbon intensity of the counterparty. In oneembodiment, for each counterparty a process may determine the carbonemissions risk to the institution by multiplying the carbon emissionsdata for the counterparty to the exposure or investment of theinstitution to the counterparty, and dividing the exposure or investmentby the sum of the equity of the counterparty and the debt of thecounterparty. A Financed Emissions Index may use exposure and measurethe amount of carbon emission sponsored indirectly by an institution orlender through its financing activities, and may be defined by Formula4:Financed Emissions=Σ_(i) Investment_(i)(Investee Equity_(i)+InvesteeDebt_(i))×Emission of Investee_(i)   Formula 4

Investment_(i) may be the lending exposure of the institution tocounterparty i. Investee Equity_(i)+Investee Debt_(i) may be the totalcapital of counterparty i, the sum of the counterparty's total equityand debt. Emission of Investee may be taken from an external databasesuch as the Trucost database, and may be the total carbon emissions peryear or other time period by the investee, not taking into accountrevenue of the investee, making Emission of Investee_(i) typicallydifferent from Carbon Intensity_(i). If a database such as Trucost doesnot have Emission of Investee for a counterparty i, it may beapproximated by, for example, by calculating the mean or median of thecarbon emissions of the companies within the industry of investee orcounterparty i. By taking the ratio between investment and totalcapital, the formula may calculate the portion of the company'soperation that is sponsored by the institution. Multiplying that ratioby Emission of investee_(i) (a measure of emissions of the investee orcounterparty, for example obtained from vendor carbon emission data,e.g., Trucost, and being in units of for example tonnes of carbon peryear) produces Financed Emission for counterparty i. Aggregating oradding, using the facto across all counterparties, produces the FinancedEmission for the institution.

Companies or counterparties may not have data provided by a service suchas the Trucost service. Embodiments may calculate or impute an estimatefor such counterparties by assigning the counterparty to an industry forwhich a carbon emissions number has been calculated (e.g., tonnes ofcarbon per year), and assigning that industry's emissions number to thecounterparty as a proxy for actual data on the counterparty. For eachcompany lacking emissions data, the company's industry is located.Carbon emissions data from the industry may be obtained, for examplefrom the Trucost service, divided by or indexed by GICS categories. Eachindustry may include a range of companies and a distribution of carbonemissions. One statistic from the distribution may be selected as aproxy for the missing carbon emission data point to be assigned to thecompany: in one embodiment the mean and median statistics are offered toa user to select, or both mean and median may be used, There may bedifferent levels of industry sectors and statistics from thedistribution that can be used. For example, four levels of GICS industrymay be used, and mean and median (e.g., the mean and median of actualdata for the industry or sector from Trucost) may be used as appropriateproxy statistics. In one embodiment, the mean for GICS level four(sub-industry) is used for such an approximation, but other levels maybe used. It has been observed by the inventors that using the mean ofthe distribution as a proxy may significantly inflate the financedemission number. The reason is that within each industry, whether it isGICS1 or GICS4, the distribution of carbon emissions is skewed to theright, leading to a higher mean than median. In some embodiments, proxydata may be based on one level (e.g., GICS4 industry) but may beaggregated to another level (e.g., GICS1 level) before display to auser.

In one embodiment, a weighted average to be used as a proxy may becalculated instead of received from an external database such as theGlobal Industry Classification Standard (GICS) industry taxonomydeveloped by MSCI and S&P. An approximation of data for a company whosecarbon emissions data cannot be found on a database such as Trucost(e.g., a “missing company”) may be found by: assigning each company thatis found in the relevant carbon database to an industry; determiningcarbon data to each industry defined in an institution's data bycomputing a weighted average or median value over companies' carbon datafor each industry; and assigning carbon data to each missing company asbeing equivalent to the weighted average carbon data for the industryfor the missing company. The weighted average may be calculated as amean or median over all companies' data used as input, and one or bothmay be used. The approximated carbon emission data may be based on theTrucost data, using the mean or median value (“statistics”) of anindustry's carbon emission to approximate the carbon emission of acompany from that industry when its name-level information is notavailable. For example, if company XYZ is a company in the Energy sectorthat the institution lends to, and the institution has XYZ's financialinformation (e.g., equity+debt), but not its carbon emission data, tocalculate the institution's financed emission by lending to Company XYZ,its carbon emission may be approximated by either the mean or median ofthe carbon emissions of all Energy sector companies for which the owninginstitution does have access to data.

To create a Financed Emissions Index, company-level exposure data (e.g.,the investment factor in Formula 4) which may be internally stored at aninstitution may be mapped to the companies' financial data, e.g., equityand debt, also for example internally stored at an institution. A ratiomay be calculated for each company to which the institution lends, ortransacts in the context of trading and counterparty derivativeexposures which represents the proportion of its carbon emission theinstitution is financing.

This same company's carbon emission information (e.g., Emission ofInvestee) may be looked up directly or imputed from the Trucost carbonemission dataset. In one embodiment, mapping of the company may beperformed by a database internal to an institution which maycross-reference companies' identifiers with standard data such as theInternational Securities Identification Number (ISIN) given in theTrucost carbon emission data. Financed emission components may be addedor aggregated across the institution's counterparties to arrive at theFinanced Emission Index.

Institutions may need to measure and disclose the amount of carbonemission sponsored indirectly by an institution or lender through itsfinancing activities in order to align with financed emissions targets(goals) or comply with climate change scenarios, currently and into thefuture.

Embodiments of the invention may project the various formulas, displays,etc. discussed herein into the future in order to calculate the variousoutputs discussed herein for future dates, using scenarios. A scenariomay include data of estimated future carbon emissions, carbon equivalentemissions, or impacts, for one or more future dates or periods (e.g.carbon emissions for an industry or sector). More than one scenario mayexist and be examined for the same future time period: e.g. a scenariowhere the energy sector has a carbon output of X, a scenario where thatsector has a carbon output of X′, etc.

In some embodiments, users may extract a user specified scenario and itsattributes including carbon emissions and sector for given level oftemperatures (degrees centigrade) and carbon emissions (CO2/metric ton)over time. One or more different scenarios may be used or selected, eachdescribing a different projection of carbon emissions over a series oftimes or time periods for a sector or other grouping, or for a company;each of these scenarios may include different carbon outputs atdifferent times. Each of these times may be used to project or adjust ameasure such as financed emissions at the relevant time in the future,adjusting the Formula 1, Formula 2, Formula 4, etc. calculation for thevarious particular points in time in the future. Thus an embodiment mayfor each scenario chosen, determine a measure such as Financed emissionsat each of a set of times in the future.

In climate change research, scenarios describe plausible trajectories ofthe different aspects of the future regarding the potential consequencesof climate change. Scenarios take into account many factors, forexample, the Intergovernmental Panel on Climate Change (IPCC) modelsclimate change scenarios based on developments in technology, changes inenergy generation and land use, global and regional economiccircumstances and population, etc. The use of scenarios not only ensuresthat starting conditions, historical data and projections are employedconsistently across the various branches of climate science, but as itrelates to financing institutions, it may effectively provide long-termgoals for financing emissions. These scenarios, which may include butare not limited to the International Institute for Applied SystemsAnalysis' Shared Socioeconomic Pathways (SSPs), may provide forecasts ofglobal mean temperatures in degrees Centigrade and CO2 in metric tons asoutputs over time under various climate risk mitigation assumptions. Forexample, under SSP1-2.6 scenario, the world is projected to reach a goalof 1.7-1.8° C. above pre-industrial levels by the year 2100, under thestricter SSP1-1.9 scenario, the world is projected to limit warming to1.3-1.4° C., with both scenarios being in compliance with the ParisAgreement of warming below 2° C. To effectively respond to long-termclimate change goals and the corresponding risk of financed emissions,an embodiment of the invention may leverage external carbon emissionsscenarios (e.g. as provided by the International Institute for AppliedSystems Analysis, or other entities, as discussed elsewhere), forexample, to project carbon emissions by sector, decades into the future,and perform calculations of counterparty level financed emissions overthe time horizon of the scenario.

An embodiment may calculate a projected Financed Emissions Index and mayuse projected exposure and measure the projected amount of carbonemission to be sponsored indirectly by an institution or lender throughits financing activities.

An embodiment may adjust measures discussed herein such as FinancedEmissions, Carbon Intensity Index, modelling, etc. using projections attimes in the future. Projecting such measures may include projecting thecarbon emissions data for individual investees or counterparties, or forclasses of investees or counterparties, according to a scenario. Ascenario may include estimated future carbon emissions data for acategory (e.g. industry, sector) at various future times T. Adjustingcurrent carbon emissions data may include multiplying the carbonemissions data by carbon emissions data at future time T for a categorywhich includes the counterparty or investee and dividing by currentcarbon emissions data for the category: a ratio of current to futurecarbon emissions of a larger category including the investee may be usedto adjust the carbon emissions for the investee according to thescenario's prediction of overall carbon emissions at future time T. Anexample projected Financed Emissions Index for an institution, for afuture time t, is shown in example Formula 4.1a and Formula 4.1b:Financed Emission_(t)=Σ_(i) Investment_(i)/(Investee Equity_(i)+InvesteeDebt_(i))×Emission of Investee_(i,t)   Formula 4.1awhereEmission of Investee_(i,t)=Emission of Investee_(i,t0)×Emission ofInvestee_(i)'s Sector_(i)/Emission of Investee_(i)'s Sector_(t0)  Formula 4.1b

Emission of Investee_(i,t) may be the projected carbon emissions of theinvestee_(i) (e.g. for all or a set of investees i=0 to n relevant to aninstitution) at a future time t starting from current time t0. Tocalculate the projected carbon Emission of Investee_(i,t) data from anexternal database, such as the SSP database, may provide theinvestee_(i)'s sector projected carbon emissions data at a future time tEmission of Investee_(i)'s Sector_(i). To calculate the future estimatedemissions of an investees or counterparty the current emissions of aninvestee_(i) may be multiplied by a ratio for the sector to which theinvestee belongs to of the current sector emissions (e.g. time=t0) tothe future sector emissions (e.g. time=t). The projected carbonemissions data Emission of Investee_(i)'s Sector_(t) need not be limitedto a sector categorization. While in one embodiment, a company's currentcarbon data is modified by future carbon data of the industry or sectorto which the company belongs, in other embodiments other future data, orother categories to which a company belongs, may be used to modifycurrent data.

A user may select from a number of different scenarios (e.g. namedscenarios), each scenario providing a different set of carbon emissionsper time period for each group, industry or sector. For example, theuser may select from a number of scenarios via a drop-down menu. Theuser-selected scenario may then be used by processes described herein,e.g. by using Formula 4.1.b, to alter emissions over time as discussedherein. A user may also enter customized scenarios, e.g. a set of carbonemissions for time periods for one or more industries.

In other embodiments, a prediction of a counterparty or investee'semissions at a future time may be based on more granular informationsuch as future emissions for a GICS category to which the investeebelongs or projected future data for that investee itself. To providemore granularity, data, if available from the Trucost service or anyother external source, may be obtained and divided by or indexed, forexample, by GICS categories. At each level of the GICS indexingstructure, more precise data may be found for an investee as GICS isdivided into a hierarchical tree structure, with GICS1 at the highest(e.g. sector) level of the hierarchical tree structure. Therefore, ifavailable, data for the lower GICS2 category (e.g. industry) may beused, instead as this data inherently follows the projections of carbonemissions more accurately for a given investee or counterparty.Projections of carbon emissions may be specified at differentgranularity levels, for example, such as at the counterparty-level,given that the counterparty projects their own carbon emissions. Whenthe projections are at the less granular sector level, it is assumedthat the emission of counterparty (investee_(i)) will change at the samerate as the emission of the sector to which the counterparty belongs to.

One of the important business planning exercises in this context issetting financed emission targets over time. This is non-trivial becausewhile end targets can be set for a point in the future, for examplecarbon neutrality by 2050, realistic but binding interim targets orbenchmarks also need to be set. The financed emission projectionsFinanced Emissions_(t) may be used as an interim target which mayoutline a specific goal a bank is mandated or on-track to achieve or mayserve as benchmarks as the business tracks its actual financed emissionsagainst scenario targets (goals) and adjusts the amount of carbonemissions sponsored. Other formulas described herein, such as CarbonIntensity Index, or based on “greeks”, may be used as targets orbenchmarks.

FIG. 12A depict carbon emissions projection for an example Energysector. In FIG. 12A two scenarios A and B are shown, each providingemissions for a sector at discrete future time periods, e.g. 2030, 2040,etc. Each of the example sectors having two example scenarios A and Bwhich may be obtained from the SSP database or any other external sourcestudying the future carbon emissions projection of either the investeeitself or the sector to which the investee belongs. The X axis is timeover a period for an example period of 80 years, and the left Y axisshows the projected carbon emissions of the sector according to twoexternal scenarios A (solid line) and B (dotted line) for eachrespective example sectors Energy and Industrial. Using the projectedcarbon emissions data, and formula 4.1 above, the Emission ofInvestee_(i,t) may be calculated for either scenario A or B for anyinvestee or counterparty which belongs to the sector. For example, aninstitution may finance a counterparty which is in the business ofelectricity generation. Therefore, that counterparty belongs to theEnergy sector and may have its carbon emissions projected by the Energysector.

Using the projected carbon emissions data for the Energy sector providedin example FIG. 12A and an example assumption that the exampleelectricity company currently produces 2,000 tons/CO2 annually, Emissionof Investee_(i,t) may be calculated for scenarios. The example presentedabove also assumes the financing institution takes a more extreme oraggressive approach in order to reduce carbon emissions financing, andtherefore selects a more rigid scenario. As such, the more rigidscenario A may be selected as the target. By applying the growth rate ofcarbon emissions under the selected scenario to a user specified timeintervals and a total time period (set of time intervals) for acounterparty or group of counterparties (e.g., portfolio or sectorlevel), the target level of emissions under the scenario for aportfolio, portfolio, sector or individual counterparty may bedetermined. For example, assume that beginning in 2020 the carbonemissions for the Energy sector was at 16,000 tons/CO2 (not shown inFIG. 12A), also assume an interim target for the year 2040 (timeinterval of 20 years) is desired and should abide by scenario A.Examining the graph, under scenario A, the Energy sector is projected toemit 1733 tons/CO2 in the year 2040 (scenario B projects 10,768 tons/CO2in the year 2040). From this data, the projected carbon emissionsEmission of Investee_(i,t) of the example electricity generation companymay be calculated for a time t=2040 under the target of scenario A.Emission of Investee_(i,t0)=2000 tons/CO2, Emission of Investee_(i)'sSector_(t)=1733 tons/CO2, Emission of Investee_(i)'s Sector_(t0)=16000tons/CO2. Therefore the projected carbon emissions of the electricitygeneration company Emission of Investee_(i,t)=2000*(1733/15000)=217tons/CO2. To obtain the projected financed emissions for thecounterparty or investee at time t (e.g. t=2040), the Emission ofInvestee_(i,t) (in the above example, 217 tons/CO2) is multiplied, as inFormula 4.1, by ratio of the lending exposure of the institution to thecounterparty i (Investments) to the sum of the counterparty's totalequity and debt (Investee Equity_(i)+Investee Debt_(i)).

In some embodiments, the scenario is mapped to an institution's internalportfolio exposures for a counterparty or group (e.g., sectoraggregation) of counterparties. Aggregating or adding, using the factori, across all counterparties, each having a corresponding sector,results in mapping the total projected financed emissions for theinstitution to the scenario, specifying the financed emissions targetsfor each time interval and total time period, shown in FIG. 12B. In FIG.12B, the total financed emissions for an institution can be seen aschanging at discrete time periods in the future for each of twoscenarios.

An optimization or modelling embodiment may alter an institution'smodelled (as opposed to actual) holdings—e.g., the amount invested ineach counterparty, sector, industry, etc. or the amount of certainexposures—to allow an institution to alter its exposures to reach atarget. For example, a bank may set limits for the amount of emissionsit will finance from counterparties or sectors. With the optimizationmethodology, this can be achieved over a specified horizon. The timehorizon can be modified to reflect long-term goals (e.g., compliancewith the 2050 Paris Accord) or shorter business planning horizons.Carbon emissions risk for a number of counterparties may be modelled by,for at least one counterparty, altering the exposure of the counterparty(e.g., the actual data, to produce modelled data) and re-determining thecarbon emissions risk to the institution for that counterparty. Updateddata may then be displayed to a user, who may then act on the model toreduce, shift or alter the actual portfolio exposure. A financedemission optimization methodology can be leveraged to provide guidanceon portfolio strategies to reduce an institution's financed emissionsover time. This can be important in setting targets or limits that canhelp the institution to achieve agreed upon levels of carbon financingin keeping with principals outlined in the Paris Agreement. Aninstitution's portfolio strategy can be optimized so that its financedemission amount is minimized over time while the portfolio still meetscertain criteria. These criteria can be modified to consider overallportfolio returns or profitability along with other financial metrics.

In an optimization or modelling process altering the exposure of acounterparty may be subject to constraints such as:

-   -   The size of the entire lending or trading portfolio cannot be        lower than a given threshold, that is, a business plan may        specify that the portfolio should grow at a given rate over        time; and    -   The exposure to any one counterparty cannot change more than a        given percentage in any one period (this could be in keeping        with contractual or other commitments).

Other constraints may be used. Since the financed emission of a bank isdetermined by not only the portfolio size, but also the portfolio mix,the portfolio can essentially be redistributed over time to meetoptimization goal(s).

The values used for such modelling at future time periods may bemodified or projected by having the underlying data (e.g. carbonemissions of a counterparty or investee, or a sector, etc.) modifiedaccording to a scenario, based on the scenario's future estimates forcarbon emissions, for example as calculated using methods discussedherein with respect to example Formula 4.1 (e.g. modifying acounterparty's emissions using Formula 4.1b). For example, for eachfuture point in time where a portfolio has been modified per theoptimization discussed above, the carbon emissions may be furthermodified using Formula 4.1b, or another measure.

The financed emission of a portfolio may be calculated using exampleFormula 5 below, similar to example Formula 4 above.Financed Emission=Σ_(i)(Counterparty Exposure_(i))/(CounterpartyEquity_(i)+Counterparty Debt_(i))×Emission of Counterparty_(i)  Formula5

Formula 5 may aggregate or add financed emissions for each counterpartyi for an institution. Assuming that the equity, debt and emissions of acounterparty remain stable over time, the financed emission amountrelated to a counterparty is solely determined by the institution'sexposure to that counterparty. In this sense, the financed emissionformulae can be reinterpreted as in example Formula 6:Financed Emission=Σ_(i) Counterparty Lending Exposure_(i)×FinancedEmission Rate of Counterparty_(i)   Formula 6Where the “Financed Emission Rate” may be defined for example as inFormula 7:Financed Emission Rate of Counterparty_(i)=Emission ofCounterparty_(i)/(Counterparty Equity_(i)+Counterparty Debt_(i))  Formula 7

To minimize the overall financed emission amount for future modeled timeperiods, lending exposures in a model (rather than in the real world)may be moved from the counterparties with high financed emission ratesto those with low financed emission rates. Such modelling a carbonemissions risk for a number of counterparties may be performed for atleast one counterparty, altering or modifying the exposure to thecounterparty and re-determining the carbon emissions risk to theinstitution for that counterparty after the exposure alteration. Thismay be performed automatically by a system such as in FIG. 1, performinga computer modelling process. In one embodiment, all counterpartiesrelevant to an institution are rank-ordered by their financed emissionrate, from low to high. Starting from both ends (lowest rate and highestrate) simultaneously, exposures of counterparties with the highestfinanced emission rates (at the bottom) are reshuffled, reordered ormoved to those with the lowest (at the top). An optimization ormodelling process may finish when the two threads meet in the middle(e.g., when the moving current lowest rate is equal or greater than tothe moving current highest rate). For example, an optimization ormodeling of carbon risk for a portfolio of counterparties may performed(e.g., automatically, by a computer processor) whereby the first rankedcounterparty (e.g., associated with highest rate of financed emissions)has its exposure reduced by an amount X that is expressed in a givencurrency, and that represents a theoretical divestment. Simultaneously,a second counterparty (e.g., with the lowest rate of financed emissions)may have its exposure increased by the same amount. The resultingexposure can be displayed for each counterparty to a user.

The process of lowering the exposures for emitters with high financedemissions rates, and raising the exposures for low financed emissionsmay continue, e.g., repetitively or iteratively, until the overallfinanced emissions have been minimized while satisfying the constraintthat the overall exposures must be at least as great as the businessplan targets.

Such modelling may be performed on other exposures described herein(e.g., CM, IR, delta, etc.) by, for at least one of the exposures,altering the exposure to the institution and re-determining the carbonemissions risk to the institution for that exposure. The altering may beperformed by for example reducing a first exposure by a percentage X toresult in an amount of reduced exposure, and for a second exposurehaving lower carbon emissions data than that of the first exposureincreasing the exposure by the amount of reduced exposure

The optimization of financed emissions may also be performed for aplurality of counterparties. In this example, the financed emission ratecan be specified for groups of counterparties and exposures can beraised or lowered for these groups to achieve the same outcome ofminimizing financed emissions for the portfolio over the planninghorizon subject to constraints on changes within a given time period(e.g., a year or quarter) as well as over the entire horizon.

In the case that no constraint is applied on how much exposure canchange for one counterparty, all exposures may be moved to thecounterparty with the lowest financed emission rate, rendering the wholeportfolio to include only one counterparty. As such, an upper bound ofhow much any one counterparty's exposure can change in one time period,or other limits, may be set. Thus the highest ranking carbon emitter mayhave up to a limit of a predefined amount of X % of the exposure to thatcounterparty transferred to the lowest carbon emitter, and then withinthe same time period, the process may move exposure away from the secondhighest carbon emitter, up to a limit of X % of the exposure to thatcounterparty. A period can be defined in any time interval, such asquarterly, yearly, etc. Similarly, proceeds from the divestment from thehigh carbon emitter may be added to the lowest carbon emitter up to apoint of a threshold X % increase to that low emitter's representationin the portfolio, at which time the second lowest emitter will havefunds added to its portfolio, and so on, on to the next lowest emitter.This process may be repeated or iterated for each time period within aprojection horizon.

As part of the optimization, the loss rate of the modified portfolio maybe output or analyzed to ensure that the profile of the portfolio isconsistent with the relevant institution's risk appetite and policy. Anexample Loss Rate is the ratio between the Expected Loss of a portfolioand the overall size of the portfolio (Loss Rate=Expected Loss/TotalExposure.); where Expected Loss, is defined as: Exposure*Probability ofDefault*Loss Given Default; other measures, including Net Present Valueof Cash Flows or expected net profits, may be used. Loss rates may beclosely monitored and compared to the loss rate of the originalportfolio, in order to ensure that no overly aggressive portfoliostrategy will be adopted in which resulting loss rates from executing onthe optimization plan yield are beyond the firm's appetite for risk.Further constraints may be applied within the framework to manageoverall portfolio returns or profits. An optimization process can beinterpreted not only from a counterparty-by-counterparty perspective,but also from a cohort-by-cohort perspective. The cohort can be definedas industry or sub-industry groups to facilitate the execution of theoptimized portfolio strategy. The optimization of financed emissions mayalso be performed for a plurality of counterparties. The financedemission rate can be specified for groups of counterparties andexposures can be raised or lowered for these groups to achieve the sameoutcome of minimizing financed emissions for the portfolio over theplanning horizon subject to constraints, for example constraints onchanges within a given time period (e.g., a year or quarter) as well asover the entire horizon, or limits to the size of a portfolio.

The optimization or modelling of financed emissions or exposures to acounterparty may also be performed using projected or future data toachieve, for example, concrete portfolio strategies which align withcommitments and growth goals, e.g. user specified commitments and goals,for a future time t. Modeling may be performed to model the change inportfolio composition (e.g. financed counterparties within a portfolio)that would occur over time to achieve carbon emissions or financedemissions targets under a selected scenario. A “backsolve” process maybe performed by computer systems as described herein for example bysetting financed emission or other carbon emissions benchmarks ortargets, then doing a grid search of the optimization parameter setsthat will lead to the desired financed emissions levels in the future.The outcome of the “backsolve” is usually a plurality of parameter setsthat can be then interpreted as high-level portfolio strategies. Whenbacksolving, scenarios may affect the input and/or output. For example,the target emissions may be defined by the scenarios chosen, forexample, choosing an aggressive (in terms of limiting temperature rises)scenario would essentially set a “target” for financial institutions toreduce their financed emissions (or other metrics) as they may need toactively re-position their exposures to get there. Other formulasdescribed herein, such as Carbon Intensity Index, or based on “greeks”,may be used as targets when backsolving. Backsolving from differenttargets will give different sets of parameters. The user may specifychanges to the target levels of emissions and financed amounts for asingle counterparty or a group of counterparties for use in assessingalternative scenarios and in developing a business plan or portfoliostrategy for achieving target levels of emissions. Business plans orportfolio strategies may need to adhere to certain constraints orlimitations. Therefore a portfolio strategy may be developed which meetsspecified constraints while simultaneously minimizing the totalprojected financed emission amount, or other measure (e.g. according toFormula 1) for future modeled time periods based on a scenario asspecified in formula 4.2:min Financed Emissions_(t) =f(Investment_(i,t), . . . , Investment_(N,t)subject to Total investments grow at a rate of J period over periodAny Investments does not change at more than the rate of K period overperiod   Formula 4.2

In formula 4.2, any lending exposures 1 to N which are part of theinstitution's portfolio (Investment₁ . . . Investment_(N)) in a modelmay be moved from the counterparties with high projected financedemission rates to those with low projected financed emission rates forany given time t. The process may continue repetitively or iterativelyuntil the overall projected financed emissions (or other measure) in themodel abides by specified constraints and have been minimized such thatit satisfies or is below the target set by the scenario. This mayproceed according to constraints. As above, example specifiedconstraints include example constraints J and K:

J—The size of the entire lending or trading portfolio for an institutioncannot be lower than a given threshold, that is, a business plan mayspecify that the portfolio should grow at a given rate J over time; e.g.J may be annual portfolio change lower threshold or requirement; and

K—The exposure to any one counterparty or investee cannot change morethan a given percentage K in any one period (for example this could bein keeping with contractual or other commitments); e.g. K may be annualexposure change limit.

Other constraints may be used. For example, shown in FIG. 13 is a graphshowing possible target pathways for projected total Financed Emissionsunder scenario A (of FIG. 12) over a decade (example time t shown: 2020to 2030). With Scenario A selected as the target scenario, a concreteportfolio strategy is worked out to allow the business to achieveinterim targets and still be able to grow the portfolio book. Theprojected financed emissions may be used to back-solve a multitude ofportfolio strategies to achieve the interim targets while stillmaintaining portfolio growth. In this example, two pathways, 1 and 2,are feasible (meets target scenario) given a set of specifiedconstraints. In FIG. 13, the X axis is time over an example period of 10years, and the left Y axis shows the projected total financed emissionsof two different combinations of the constraints J and K (resulting intwo pathways). The first combination of J and K, possible pathway 1,provides constraints which achieve the interim growth targets whilemeeting target scenarios (e.g. below the target line and therefore meetsthe scenario) the projected financed emission target scenario shown asthe solid line. In this example, possible pathway 1 is below the targetscenario and has example constraints J=5% and K=6%. The secondcombination, possible pathway 2, also provides constraints which achievethe interim growth rate while meeting projected financed emissiontargets. In this example, possible pathway 2 has example constraintsJ=5% and K=6.5%, well below the target scenario and provides lowerfinanced emissions than possible pathway 1 while maintaining the growthrate. As is evident, in this example, assuming everything else constant;for the constraint K, the more that the exposure to any one counterpartycan change, the more aggressive a portfolio strategy may divest from thecounterparties with high projected financed emissions rates and investto those with low projected financed emissions rates. For example, byexamining possible pathway 2 given that the constraint K is higher invalue than constraint K in pathway 1 (e.g. allows more aggressivedivestment/investment), it should be evident that pathway 2 shouldproject lower financed emissions for the institution. Although this isthe general, expected trend, situations where the opposite is true mayoccur. Therefore, the constraints are not limited to any specific trendand are described to illustrate an embodiment of the invention.

The total financed emissions in FIG. 13 may be adjusted based on ascenario. For example, at each time for which financed emissions areplotted, the carbon emissions used in the underlying calculations may bemodified or projected according to that specific time period in thechosen scenario. E.g., the emissions plotted at time year=2023 may beadjusted using year 2023 figures as applied to Formula 4.1b.

In this example, only two combinations of example constraints (e.g. Jand K) are described, however, the constraints may be any number ofconstraints in any combination which meet the target scenario, andconstraints may be other than the specific J and K described.

Combinations of constraint parameters may be found by, for example, agrid search, exploring all or a specified discrete set of combinationsof a set of constraint parameters (e.g. J and K) which will lead tofinanced emissions that are lower than the set goals, thus achieving thefinanced emissions targets (e.g., have emissions that are lower than anypathway represented by the line of “target scenario,” solid line in FIG.13). As part of a grid search, combinations, for example, involving theconstraint K, may limit how much an institution may invest/divest into acounterparty at each time period (e.g. year). Goals, benchmarks ortargets may be, for each or a set of time periods, the carbon goals, asset in the various ways described herein (e.g. Carbon Intensity Index,Financed Emissions, “greek” based emissions measures, etc.), andconstraints may be found which meet or exceed (e.g. have emissions fallbelow) the goals at each time period.

As in Formula 4.2, in order to minimize financed emissions, institutionshave a motivation to divest from high carbon emissions counterpartiesand invest in low carbon emissions counterparties. Therefore, as anexample, if the constraint K is too low, then the institution may notmeet scenario targets within the defined time period t as in certainsituations, the institution may only divest minimally, with thepossibility of not meeting scenario targets. If on the other hand theconstraint K is high, then institutions may substantially divest,meeting target scenarios. The grid search enables finding thecombinations which meet the target scenario. Although a grid search maybe infinite, realistically, a grid search may be done in manageableincrements. For example, a grid search may be performed for a set ofdiscrete combinations of constraints using some discrete interval toproduce a manageable number of constraints, e.g. all combinations of(J,K) where J is between 0% and 10% and K is between 5% and 20%, with asearch interval of 1%. This would produce about 150 combinations of(J,K), and can be tested quickly with a script. As a result, a multitudeof concrete portfolio strategies may be generated that can achieve thefinanced emissions target. For example, based on the selectedcombination of constraints, different portfolio strategies may varyinglydivest or invest in different combinations of industries or investees.For example, a portfolio strategy may heavily divest from carbonintensive counterparties and slightly increase investments in multiplenon-carbon intensive sectors (e.g. health care, communication services,information technology, etc.). Another portfolio may strategize theopposite, divesting slightly from multiple non-carbon intensive sectorsto heavily increase investments in carbon-intensive sectors. A multitudeof portfolio strategies may be developed, suited to an institution'sneeds or constraints. The grid search of combinations of constraintparameters which lead to the desired financed emissions levels maybacksolve desired constraints which may be interpreted as high-levelportfolio strategies. Using backsolving, constraints may be found whichmay tell the institution how much may be divested per counterparty overtime; how much growth may be achieved, etc., to meet certain goals,based on certain scenarios.

Various key parameters may be specified for an optimization, for examplethe upper bound of how much a single counterparty's exposure can changein one period (e.g., in %); the required period-over-period growth rateof the overall size of the lending portfolio (e.g., in %); the number ofperiods simulated into the future; and the length of each period (e.g.,quarters, years or increments of years). Other parameters may be used.The modelling or simulation over time may be displayed.

FIG. 5 depicts a display, e.g., presented to a user, showing a modelover time of a portfolio as counterparty weights are changed, accordingto embodiments of the present invention. A model to produce the displayof FIG. 5 included the example constraints that any counterparty'sexposure cannot change by more than 10% within a single period or acrossperiods; the total exposure of the portfolio (e.g., exposures to anumber of counterparties for an institution) must increase by 5% by theend of every period reflecting business growth targets for each period;and there are in total, 4 periods, defined in quarters, providing aprojection into the future. In FIG. 5 the X axis is time, the left Yaxis shows exposure in millions of dollars and the right Y axis showsthe financed emissions summed across counterparties in the portfolio, inmillions of tons of emitted carbon. Referring to FIG. 5, it can be seenvia the downward sloping line 500 that over the 4 periods, the financedemission amount of the portfolio in question decreased from 48.3 mm tonsto 33.1 tons, and via the upward sloping line 502 the exposure (e.g.,money lent) increased. In this model, the loss rate of the portfolio,e.g., Expected Loss divided by portfolio size, also decreased from0.4879% to 0.4456%. The optimization shows to the user that theinstitution is able to reduce its financed emissions, and grow theportfolio from $48B to $59B yet still manage expected losses.

A display to a user may show the portfolio's exposures by sector as wellas financed emission by sector, before and after, and during all periodsof, the optimization. For example, such an optimization may show most ofthe financed emission reduction comes from Utilities, Energy, Materialsand Industrials, with their sector exposures also reduced. This isintuitive as those sectors have the highest carbon emission as well ascarbon intensity, and their business models are reliant on the emissionof greenhouse gasses.

A user may view reports showing carbon emissions data and interact withthe system for example to operate models. For example, a display mayprovide a Carbon Intensity Index for the owning institution, possibly inconjunction with financial information such as the owning institution'scredit portfolio. A display may provide a Carbon Intensity Index fordifferent institutions including the owning institution compared againsteach other, with other information such as a comparison of their credit,or other portfolio compositions. A “Carbon Index Scenario Analysis”display may allow users to change the owning institution's creditportfolio, e.g., manually model, and see how its Carbon Intensity Indexis impacted by such changes, to model alternative scenarios. A “FinancedEmissions” section may display the financed emission information, e.g.,for individual counterparties and for an institution's entire creditportfolio.

FIG. 6 depicts a display, e.g., presented to a user, showing carbonintensity over time for an owning institution, and other data, accordingto embodiments of the present invention. In the example shown in FIG. 6,as with other examples described allowing for user input, a user mayenter data or selections for example via a GUI (graphical userinterface) executed by a computer such as a user terminal 30. Box 600gives the user options to customize, e.g., via user input to a userterminal, the carbon intensity index calculation, including time rangeand type of carbon intensity index (e.g., Formula 1 vs. Formula 2).Panel 602 displays the breakdown of the owning institution's creditportfolio by industry, in this example at two points in time, the pointsin time according to box 604, which enables the user, via user input, tosee the breakdown at different points in time, e.g., to select dates.Graph 606 displays carbon intensity over time for the owninginstitution, for example the institution's entire portfolio or anotherselection of the owning institution exposures. The X axis representstime, e.g., months or quarters, and the Y axis represents the carbonintensity index value, in units of for example using tonnes carbon (in ayear)/$mm revenue/time period (e.g., a year). Line or other marker 608depicts carbon intensity using Scope 1 data; line or other marker 610depicts carbon intensity using Scope 2 data; line or other marker 612depicts carbon intensity using Scope 3 data; and line or other marker614 depicts carbon intensity Scope 1+Scope 2₊Scope 3 data. As with othergraphs and data displays discussed herein, the graphs may beinteractive, such that a user may provide input to a GUI to zoom in, orhover using a pointing device over a point and have displayed theunderlying numerical information related to the lines on the graph, orcompare different lines on a graph. The carbon intensity may be depictedweighted by sector, or in another manner. User input may cause aprocessor (e.g., processors 12 as in FIG. 1) to perform calculations tochange the displayed carbon information in graph 606. As with othergraphs or displays, the FIG. 6 graph may be modified according to carbonestimates taken from a scenario: e.g. if the displays in FIG. 6 relateto future dates, the specific numbers plotted for specific future timesmay be modified according to those specific future times in a chosenscenario, modified according to, for example. Formula 4.1a.

The display of graph 606, and other displays shown herein, may beadapted to display carbon intensity measure based on other assets,investments or exposures other than those depicted in FIG. 6. Forexample, an embodiment may calculate and display a graph such as thatshown in graph 606, but with carbon exposure based on other exposuressuch as trading book exposures such as CE, CM, EQ, FX, interest rate,CR; or deltas (or other “greeks”, or other exposures discussed herein.

FIG. 7 depicts a display, e.g., presented to a user, allowing modellingof carbon data at a point-in-time, according to embodiments of thepresent invention. A scenario analysis or modelling embodiment may allowfor example point-in-time and forward-looking options. A point-in-timefunction may enable users to do what-if analysis based on the owninginstitution's historical credit portfolio to answer the question: if theportfolio is changed by a certain amount, what is the impact on theowning institution's carbon intensity index? Box 700 gives the useroptions to change the modelled credit portfolio of the owninginstitution at user-selectable points in history, selecting whichindustries (or individual companies or other holdings) to change and byhow much, and a date from which to start performing the scenarioanalysis: the real model is not changed by this process, until andunless, in reaction to a display such as in FIG. 7, an institutionchanges the actual allocation. User input, as with other figures andfunctions, may be provided by a user inputting data at a user computeror terminal, and user input may cause a processor (e.g., processors 12)to perform calculations to change the displayed information, such ascarbon information in graph 712. After the parameters are selected orinput by the user, the boxes 702 and 704 display the portfoliocomposition of the institution by category (e.g., industry, sector,counterparty, etc.) before and after the change provided by the user inbox 700. Box 710 allows the user to choose how to display the modifiedcarbon intensity index series (e.g., Formula 1 vs. Formula 2). The usercan choose the time range and type of carbon intensity index.

Graph 712 the carbon intensity index calculated over time as the userspecified via user input, for the owning institution, for example theinstitution's entire portfolio or another selection of the owninginstitution exposures Line or other marker 720 depicts carbon intensityusing Scope 1 data; line or other marker 722 depicts carbon intensityusing Scope 2 data; line or other marker 724 depicts carbon intensityusing Scope 3 data; and line or other marker 726 depicts carbonintensity Scope 1+Scope 2₊Scope 3 data. The portions of the lines afterthe time corresponding to the date input in box 700 represents shockeddata, e.g., updated carbon intensity index values given the specifiedportfolio exposure change at the specified date. Line portions 720′,722′, 724′ and 726′ represent shocked data for a point-in-time scenarioanalysis, where the dollar exposure change selected in area 700 ($3,000mm in this example) is an instantaneous shock to the portfolio as of theselected date, but which does not alter the portfolio profile of anyother dates. The X axis represents time, e.g., months or quarters, andthe Y axis represents the carbon intensity index value, in units of forexample using tonnes carbon (in a year)/$mm revenue/time period (e.g., ayear).

A forward-looking scenario or model may enable users to perform scenarioanalysis on a forward-looking, in time, basis. The user can specify theowning institution's portfolio composition in the future, for example,in the upcoming four quarters. Based on the user specified portfolio, asystem (e.g., as in FIG. 1) may calculate the owning institution'scarbon intensity index in the selected time period—e.g., the upcomingyear. The user may be able to specify how the exposure for one sectorchanges, or how exposures for all sectors change. A forward-lookingcarbon intensity index may be calculated using the last available carbonintensity in the relevant data, such as the Trucost data.

FIG. 8 depicts a display, e.g., presented to a user, allowing modellingof carbon data over future time periods, according to embodiments of thepresent invention. In box 800 a user can input or specify how a model ofan owning institution's portfolio changes or evolves over a futureperiod, e.g., the upcoming year. For example the user may be able tospecify to which sector or counterparty to apply the changes (or to theentire portfolio), and also by how much in each period such as quarter;the processor may calculate the changes, the calculations may beperformed e.g., by server 10, and the carbon exposure over time may bedisplayed in area 810. For example, a user may enter exposure changesin, e.g., positive or negative currency values (e.g., $mm), orpercentages, in a field for each time period in box 800 to reflectincrease or decrease in exposure, and the selected sector orcounterparty would have its holdings in the owning institution'smodelled (not actual) portfolio changed by the entered dollar amount orpercentage for each time period. For example, if sector industrials isselected, and in quarter 1 of the upcoming year 5% is selected, thedisplay in area 810 will show the carbon measurement (e.g., CarbonIntensity Index) resulting from an increase of 5% in the modelledportfolio during that time period.

A display area 802 may display portfolio composition over the upcomingtime period, e.g., in the next one year. Based on the user selectedchanges per period in area 800. In one embodiment the composition isbroken down by sector; in other embodiments other divisions may be used.

Box 808 allows the user to choose how to display the modified carbonintensity index series (e.g., Formula 1 vs. Formula 2). The user canchoose the time range and type of carbon intensity index. Graph 810shows the carbon intensity index calculated over time as the userspecified via user input, for the owning institution, for example theinstitution's entire portfolio or another selection of the owninginstitution exposures. Graph 810 shows the carbon intensity indexcalculated over time as the user specified via user input, for theowning institution, for example the institution's entire portfolio oranother selection of the owning institution exposures Line or othermarker 820 depicts carbon intensity using Scope 1 data; line or othermarker 822 depicts carbon intensity using Scope 2 data; line or othermarker 824 depicts carbon intensity using Scope 3 data; and line orother marker 826 depicts carbon intensity Scope 1+Scope 2+Scope 3 data.The portions of the lines after the time marked 828 represent projecteddata, as opposed to the real data shown up to time point 828. The X axisrepresents time, e.g., months or quarters, and the Y axis represents thecarbon intensity index value, in units of, for example, using tonnescarbon (in a year)/$mm revenue/time period (e.g., a year).

The display in graphs 712 and 810 may display Carbon Intensity Index(e.g., formulas 1 and 2 above) but may display other measures of carbonemissions.

A scenario analysis tool as depicted in FIG. 8 can be leveraged toproject the impact of an optimized portfolio strategy on the CarbonIntensity Index of a bank. In facilitating these types of what-ifanalyses, it lends insight into the future climate risk profile of abank given a hypothetical portfolio strategy.

In another example the user may specify how much in each period such asquarter to adjust a model automatically, and iteratively over one ormore future periods. For example, a user may enter a certain percentage,such as 5%, by which the holdings in the most carbon intensive sector orcounterparty is reduced in a modelled portfolio in each time period(typically up to a limit reduction in that portfolio, e.g., as apercentage reduction limit), with an equivalent dollar amount to theamount being divested from the high carbon emitter being added to theleast carbon intensive sector or counterparty during that period(typically up to a limit) For example, if in quarter 1 of the upcomingyear sector A emits the most carbon per dollar of revenue and sector Bemits the least carbon per dollar of revenue, in quarter 2 the holdingsfor sector A would be reduced by 5% and the resulting theoreticalproceeds from the sale would be invested in to sector B. Each reduction(e.g., divestment) and addition may be up to a threshold or limitpercentage, at which point the addition process moves to the next lowestcarbon emitting sector, and the reduction process moves to the nexthighest carbon emitting sector, according to which limits are reached.The changes may be displayed in area 810. Such an embodiment may provideinformation to a user telling the user how and where to divest or investto improve a real-world portfolio's carbon exposure over time.

An embodiment may calculate and display financed emissions (e.g., basedon Formula 4). A Distribution Analysis may display to a user to see thefinanced emission of a specific counterparty of the owning institutionand for example how it compares with companies of the same industry. AnAttribution Analysis may give breakdowns of an owning institution'sfinanced emission by different criteria.

FIG. 9 depicts a display, e.g., presented to a user, allowing aDistribution Analysis calculation and display, where a user analyzes thefinanced emission of a specific counterparty of an institution,according to an embodiment of the present invention. The display as inFIG. 9 allows display of carbon emissions risk, e.g., financedemissions, for a plurality of counterparties as a normalizeddistribution, indicates a point on the distribution corresponding to aselected counterparty. A user may input or specify a specificcounterparty in area 900, for which a system such as that in FIG. 1 mayperform calculations, and it may be displayed to the user where thatcounterparty lies in a distribution of financed emissions for examplewithin the counterparty's industry compared to other counterparties inthat industry and possibly also in or limited to the owninginstitution's portfolio.

In the case that the selected company has no emissions data (e.g., thecompany is not listed in the Trucost database), the data may be proxydata, in which case whether the data is mean or median based may beuser-selected and/or displayed and proxy data will be displayed. Graph902 depicts an example financed emission distribution using a medianproxy for all companies in the sector of the counterparty selected inarea 900 (and possibly also limited to companies in the owninginstitution's portfolio), and includes line or other marker 904 showingwhere the selected company falls in the distribution. The X axis ingraph 902 shows the financed emission in tons C02, for example perFormula 4 above, using the median proxy as an input. The Y axis depictsa normalized frequency of counterparties corresponding to the data atthe specific point on the X axis; the Y axis data may be a histogram,normalized representation of number of companies that in a certain databin. In the case that the selected company does have known, actualemissions data, actual emissions data will be used. However, even ifknown emissions data is used, typically separate mean and mediandisplays in graphs 902 and 906 (instead of one display showing theselected company compared with industry peers) may be shown, as peerdata typically includes some companies with proxy as opposed to knowndata. In some embodiments, even if peer data includes proxy data, onecombined graph may be shown.

Graph 906 depicts an example financed emission distribution using a meanproxy for all companies in the sector of the counterparty selected inarea 900 (and possibly also limited to companies in the owninginstitution's portfolio), and includes line or other marker 908 showingwhere the selected company falls in the distribution. The X axis ingraph 906 shows the financed emission in tons C02, for example perFormula 4 above, using the mean proxy as an input. The Y axis depicts anormalized frequency of companies falling within the emissions levelsshown by the X axis.

Area 910 may show more detailed information for the specified company,for example including its sector, country, rating, carbon emission,financed emission, and whether the emission data is proxy/approximatedor not (e.g., if the data is from Trucost it is not approximated).

Other analyses and displays may be provided to a user. An AttributionAnalysis may provide a user with an institution's financed emission bydifferent criteria, for example sector, industry, counterparty, etc.Financed emissions or other carbon emissions measures produced by theselected criteria entities may be displayed, along with data supportingemissions graphs. For example, if sector is selected as an attributiontype criteria, the amount and proportion (e.g., in a graph) of eachsector's financed emissions (or another criteria) may be displayed, forexample using both a mean proxy and a median proxy, or based on actualdata.

A user may search by company, industry, or other grouping to look up thecarbon emission data for a specific company, industry, or othergrouping, and choose what Scope of carbon emission to be displayed(e.g., Scopes 1, 2 or 3, or all Scopes), and a time range (e.g., twodates defining a range). A graph may be displayed showing the selectedcompany's (or industry, etc.) carbon emission over the time range,possibly compared to the industry average for the industry including thecompany. Detailed data supporting the graph may also be displayed.

A Distribution Analysis may display to a user the carbon intensityrelevant to one company. Such a display may show for a givencounterparty or company how it compares with its peers in terms ofcarbon emission and carbon intensity. In such a GUI or display a usermay choose the company, Scope (e.g., Scopes 1, 2 3 or all) of carbonemission and carbon intensity, and time range or at which point of timethe comparison should be based on. A user may enter sector and country,or other category definitions, to define the population of the peercompanies. A display of graphs may demonstrate how the company at issueperforms relative to its peers by, for example, absolute or total carbonemissions, or intensity distribution, with the X axis indicating whereon a distribution (e.g., a normalized distribution) the selected companyfalls, with a line or other marker indicating where the company falls,and the Y axis depicts frequency within a normalized distribution. Forexample graphs depicting carbon emissions and carbon intensity may beprovided.

FIG. 10 shows a flowchart of a method according to embodiments of thepresent invention. The operations of FIG. 10 may be performed by thesystem shown in FIG. 1, but other systems may be used.

Referring to FIG. 10, in operation 1000, a process may determine carbonemissions data for one or more counterparties or other entities orholdings (e.g., equity position, FX position, etc.), e.g., by accessinga vendor database such as Trucost or by approximating data for certaincompanies. In another embodiment, in operation 1000, carbon emissionsdata may be determined for the future using projected carbon emissionsdata received by accessing vendors. In an alternate embodiment,emissions data may be determined which corresponds to exposures otherthan counterparties, such as interest rates or commodities.

In operation 1010 for each counterparty, exposure or other entity, orholding (e.g., equity position, FX position, etc.), a process maydetermine the carbon emissions risk to the institution by for examplemultiplying the carbon emissions data for the counterparty by theexposure of the institution to the counterparty. The exposure or riskmay be divided by the exposure by the total amount of loans made by theinstitution. The exposure or risk may be divided by the sum of theequity of the counterparty and the debt of the counterparty.

In operation 1020 carbon emissions risk over time may be displayed,e.g., based on one of the formulas described herein. In someembodiments, risk may be modelled by altering an internal representationof a portfolio or exposures without altering actual exposures. Forexample, a process may model carbon emissions risk for a number ofexposures or counterparties by, for at least one counterparty, alteringthe exposure of the institution to the counterparty and re-determiningthe carbon emissions risk to the institution for that counterparty, andthen displaying this modelled risk.

In operation 1030 carbon emissions for a counterparty or exposure may bedisplayed. For example, a system may display carbon emissions risk for anumber of counterparties as a normalized distribution, indicating anindication or point on the distribution corresponding to a counterpartyselected or input by a user.

In operation 1040 a party or user may take action on an actual portfoliobased on a display or output. For example, a user may sell or purchaseassets, or lower or increase exposure to a specific counterparty or in asector, in order to alter carbon risk, based on information gleaned froma display or output. Output information may be applied to determine andcreate optimal investment strategies for managing an institution'sexposures to carbon risk over time.

Reference is made to FIG. 11, showing a high-level block diagram of anexemplary computing device according to some embodiments of the presentinvention. Computing device 100 may include a controller 105 that maybe, for example, a central processing unit processor (CPU) or any othersuitable multi-purpose or specific processors or controllers, a chip orany suitable computing or computational device, an operating system 115,a memory 120, executable code 125, a storage system 130, input devices135 and output devices 140. Controller 105 (or one or more controllersor processors, possibly across multiple units or devices) may beconfigured to carry out methods described herein, and/or to execute oract as the various modules, units, etc. for example when executing code125. More than one computing device 100 may be included in, and one ormore computing devices 100 may be, or act as the components of, a systemaccording to embodiments of the invention. Various components,computers, and modules of FIG. 1 may be or include devices such ascomputing device 100, and one or more devices such as computing device100 may carry out functions such as those described in FIG. 10 andproduce displays as described herein.

Operating system 115 may be or may include any code segment (e.g., onesimilar to executable code 125) designed and/or configured to performtasks involving coordination, scheduling, arbitration, controlling orotherwise managing operation of computing device 100, for example,scheduling execution of software programs or enabling software programsor other modules or units to communicate.

Memory 120 may be or may include, for example, a Random Access Memory(RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a SynchronousDRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, avolatile memory, a non-volatile memory, a cache memory, a buffer, ashort term memory unit, a long term memory unit, or other suitablememory or storage units. Memory 120 may be or may include a pluralityof, possibly different memory units. Memory 120 may be a computer orprocessor non-transitory readable medium, or a computer non-transitorystorage medium, e.g., a RAM.

Executable code 125 may be any executable code, e.g., an application, aprogram, a process, task or script. Executable code 125 may be executedby controller 105 possibly under control of operating system 115. Forexample, executable code 125 may configure controller 105 to calculateand display carbon emissions risk data and perform other methods asdescribed herein. Although, for the sake of clarity, a single item ofexecutable code 125 is shown in FIG. 11, a system according to someembodiments of the invention may include a plurality of executable codesegments similar to executable code 125 that may be loaded into memory120 or another non-transitory storage medium and cause controller 105,when executing code 125, to carry out methods described herein.

Storage system 130 may be or may include, for example, a hard diskdrive, a CD-Recordable (CD-R) drive, a Blu-ray disk (BD), a universalserial bus (USB) device or other suitable removable and/or fixed storageunit. Data such as carbon spend data, user data, merchant data andfinancial transactions may be stored in storage system 130 and may beloaded from storage system 130 into memory 120 where it may be processedby controller 105. Some of the components shown in FIG. 11 may beomitted. For example, memory 120 may be a non-volatile memory having thestorage capacity of storage system 130. Accordingly, although shown as aseparate component, storage system 130 may be embedded or included inmemory 120.

Input devices 135 may be or may include a mouse, a keyboard, amicrophone, a touch screen or pad or any suitable input device. Anysuitable number of input devices may be operatively connected tocomputing device 100 as shown by block 135. Output devices 140 mayinclude one or more displays or monitors, speakers and/or any othersuitable output devices. Any suitable number of output devices may beoperatively connected to computing device 100 as shown by block 140. Anyapplicable input/output (I/O) devices may be connected to computingdevice 100 as shown by blocks 135 and 140. For example, a wired orwireless network interface card (MC), a printer, a universal serial bus(USB) device or external hard drive may be included in input devices 135and/or output devices 140.

In some embodiments, device 100 may include or may be, for example, apersonal computer, a desktop computer, a laptop computer, a workstation,a server computer, a network device, or any other suitable computingdevice. A system as described herein may include one or more devicessuch as computing device 100.

Embodiments may improve carbon footprint analysis technology byproviding a quantitative and repeatable measurement system to helpmanage financial institutions' exposure to carbon emission and otherclimate change related risks. Embodiments may improve the technology ofcarbon data gathering and calculation to translate the long-term risksassociated with carbon emissions into actionable plans that are based oncarbon emissions data and the financial exposures of the institution.While carbon emission data can be sourced from external sources, andfinancial exposure data are usually available from any financialinstitution's internal databases or public filings, there is no priormethod to marry these sources of information into actionable insights.Information provided, such as shown in FIGS. 2-9, may allow or prompt aninstitution to reduce the weights of the carbon-intensive sectors suchas energy, materials and utilities in its portfolio. The use of futurescenario information may improve the technology of forecasting carbonemissions by including more accurate information, predicted to changeover time, in the prediction. Modeling carbon emissions risk or othercarbon metrics over time may improve the technology of carbon emissionsand risk modeling by allowing users to produce models based on changingportfolios, changing scenarios, possibly incorporating the complexitiesof constraints.

In the description and claims of the present application, each of theverbs, “comprise”, “include” and “have”, and conjugates thereof, areused to indicate that the object or objects of the verb are notnecessarily a complete listing of components, elements or parts of thesubject or subjects of the verb. Unless otherwise stated, adjectivessuch as “substantially” and “about” modifying a condition orrelationship characteristic of a feature or features of an embodiment ofthe disclosure, are understood to mean that the condition orcharacteristic is defined to within tolerances that are acceptable foroperation of an embodiment as described. In addition, the word “or” isconsidered to be the inclusive “or” rather than the exclusive or, andindicates at least one of, or any combination of items it conjoins.

Descriptions of embodiments of the invention in the present applicationare provided by way of example and are not intended to limit the scopeof the invention. The described embodiments comprise different features,not all of which are required in all embodiments. Embodiments comprisingdifferent combinations of features noted in the described embodiments,will occur to a person having ordinary skill in the art. Some elementsdescribed with respect to one embodiment may be combined with featuresor elements described with respect to other embodiments. The scope ofthe invention is limited only by the claims.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents may occur to those skilled in the art. It is, therefore, tobe understood that the appended claims are intended to cover all suchmodifications and changes as fall within the true spirit of theinvention.

The invention claimed is:
 1. A method for determining the carbonemissions risk to an institution of one or more counterparties to theinstitution, the method comprising: receiving a scenario for carbonemissions; determining carbon emissions data for one or morecounterparties; for each counterparty, determining the carbon emissionsrisk to the institution by multiplying the carbon emissions data for thecounterparty by the exposure of the institution to the counterparty,wherein the carbon emissions data is projected according to thescenario; and modelling a carbon emissions risk for one or morecounterparties by: for at least a first counterparty, reducing theexposure of the institution to the first counterparty by a percentage Xto result in a currency amount of reduced equity, and for a secondcounterparty having lower carbon emissions data than that of the firstcounterparty increasing the exposure of the institution by the currencyamount of reduced equity; and for at least one counterpartyre-determining the carbon emissions risk to the institution for thatcounterparty.
 2. The method of claim 1, wherein modeling a projectedcarbon emission risk comprises performing a grid search of acombinations of constraints.
 3. The method of claim 1, wherein thecarbon emissions data is projected for the counterparty based on thecategory to which the counterparty belongs to.
 4. The method of claim 1,comprising displaying carbon emissions risk over time.
 5. The method ofclaim 1, wherein the projected carbon emissions data is used tobacksolve a combination of constraints according to emissions targets.6. The method of claim 1, wherein projecting carbon emissions dataaccording to the scenario comprises, for a future time T, multiplyingthe carbon emissions data by carbon emissions data at time T for acategory including the counterparty and dividing by current carbonemissions data for the category.
 7. A system for determining the carbonemissions risk to an institution of one or more counterparties to theinstitution, the system comprising: a memory and; a processor configuredto: receive a scenario for carbon emissions; determine carbon emissionsdata for one or more counterparties; for each counterparty, determinethe carbon emissions risk to the institution by multiplying the carbonemissions data for the counterpart), by the exposure of the institutionto the counterparty, wherein the carbon emissions data is projectedaccording to the scenario; and model a carbon emissions risk for one ormore counterparties by: for at least a first counterparty, reducing theexposure of the institution to the first counterparty by a percentage Xto result in a currency amount of reduced equity, and for a secondcounterparty having lower carbon emissions data than that of the firstcounterparty increasing the exposure of the institution by the currencyamount of reduced equity; and for at least one counterparty,re-determining the carbon emissions risk to the institution for thatcounterparty.
 8. The system of claim 7, wherein modeling a projectedcarbon emission risk comprises performing a grid search of acombinations of constraints.
 9. The system of claim 7, wherein thecarbon emissions data for the counterparty is projected based on thesector to which the counterparty belongs to.
 10. The system of claim 7,wherein the processor is configured to display carbon emissions riskover time.
 11. The system of claim 7, wherein the projected carbonemissions data is used to backsolve a combination of constraintsaccording to emissions targets.
 12. The system of claim 7, wherein theprocessor is configured to project carbon emissions data according tothe scenario, for a future time T, multiply the carbon emissions data bycarbon emissions data at time T for a category including thecounterparty and divide by current carbon emissions data for thecategory.
 13. A method for determining the carbon emissions risk to aninstitution of one or more exposures to the institution, the methodcomprising: determining carbon emissions data corresponding to one ormore exposures to the institution; determining the carbon emissions riskto the institution by multiplying the carbon emissions data for theexposures by the exposures, wherein the carbon emissions data for theexposures is based on future data; and modelling a carbon emissions riskfor one or more exposures by, for at least a first exposure, reducingthe exposure of the institution to the first exposure by a predeterminedamount to result in reduced equity, and for a second exposure havinglower carbon emissions data than that of the first exposure increasingthe exposure of the institution by the predetermined amount; andre-determining the carbon emissions risk to the institution for at leastone exposure.
 14. The method of claim 13, wherein the future datacorresponding to one or more exposures to the institution is based onthe sector to which the exposure belongs to.
 15. The method of claim 13,wherein determining the carbon emissions risk to the institutioncomprises, for a future time T, multiplying the carbon emissions data bycarbon emissions data at time T for a sector including the exposure anddividing by current carbon emissions data for the sector.